There is currently (2016) intense interest in unmanned and automated vehicles. Companies such as Google (Alphabet Inc.) are road-testing driverless cars. Companies such as Tesla are implementing automated highway driving features in vehicles where a human driver is present. Remotely-controlled flying drones, with remotely-viewable cameras and other appendages, have been a consumer phenomenon. The United States Armed Forces are well-known to use large unmanned aerial drones in their combat operations. Such technology may be applied to boats and ships as well. Via consumer culture, such as mobile phones and so forth, interest in artificial intelligence and related applications is experiencing a resurgence. For at least two years early-adopters have claimed that automated vehicles are “already here,” perhaps prematurely. However, the roads are still primarily filled with conventional cars, and it is a remarkable event to see something like a driverless test car on the road, something that can only happen in the few areas near where they are produced.
The popular optimistic belief seems to be that automated cars will be eventually everywhere, perhaps in only a few years. Among these expectations is that entire job industries will be made obsolete: the consensus opinion is that we will no longer need semi-truck drivers, or cab drivers, or pizza delivery men, or any number of occupations related to the human operation of vehicles. Some believe that personal ownership of cars will go away in favor of leasing summonable automated vehicles.
Related to this belief is the notion of a “fleet,” that is, that the automated vehicles (again cars as an example) will be grouped by purpose, however general, so that groups of them are responsible for delivering pizzas, or for hauling semi-trailer loads, or for delivering furniture, or for carrying human passengers (like taxis or buses), or by merely being owned by the same firm, and so on.
Various schemes have been created for semi-autonomous vehicles to determine when they require human intervention. For example U.S. Pat. No. 8,718,861 “Determining when to drive autonomously.” Prior inventions assume that the human controller of the semi-autonomous vehicle is actually inside the vehicle, and that there is one human controller per vehicle. This doesn't always make commercial sense, unless the vehicle cargo is human, and the vehicle cargo/potential controller of the vehicle is as alert as he would be if piloting the vehicle himself. The reality is, alert human attention is a resource which autonomous and semi-autonomous vehicles are supposed to conserve and minimize to realize efficiencies, so having alert human attention centralized and remote can enable higher degrees of automation and commercial utility in semi-autonomous vehicle fleets.
US patent application US20060271246A1 “Systems and methods for remote vehicle management” is a specific technology for remote monitoring of non-autonomous vehicles. A variation of this specific technology may be used for the end control terminal sensors and telemetry though some other specific end terminal technology must also be used for dynamic vehicle control.
US patent application US20040030448 “System, methods and apparatus for managing external computation and sensor resources applied to mobile robotic network” pertains to the use of external sensors and computational capabilities in robot swarms, where the swarms may have limited inter-communication and computational capabilities.
U.S. Pat. No. 5,367,456 “Hierarchical control system for automatically guided vehicles” describes a hierarchy of programs for controlling automated vehicles to provide differing levels of computer control to fleets of vehicles. The patent postulates that some of the functions may not be possible in the computers deployed in the automobiles, so they may be provided by remote computer control, rather than remote human control.
US patent application US20040030571 “System, methods and apparatus for leader-follower model of mobile robotic system aggregation” postulates mobile robots using follow the leader algorithms to reduce the computational complexity of piloting a large fleet of vehicles.
US patent application US20130041576 “Systems and Methods for Semi-Autonomous Convoying of Vehicles” postulates a different specific scheme of aggregating vehicle convoys, particularly as an energy saving technology, though also to save computational resources. Our scheme may take advantage of vehicle convoys as a way of simplifying and reducing the number of human controllers required for a large fleet of vehicles.
U.S. Pat. No. 6,024,142 “Communications system and method, fleet management system and method, and method of impeding theft of fuel” is a passive monitoring system for detecting and mitigating theft of fuel using a specific RFID technology. This type of system essentially tags the vehicle using RFID monitoring and only allows certain vehicles to take on fuel. The communication link is between vehicle and fuel depot; our method is a more broad communication link back to central control location.
The use of human agents to service semi-automated computer equipment has a long history in the Call Center industry. For example, U.S. Pat. No. 6,347,139 “System for automatically routing calls to call center agents in an agent surplus condition based on agent occupancy”—is a system for routing calls to agents based on their availability. U.S. Pat. No. 6,587,556 “Skills based routing method and system for call center” is a patent for routing telephone calls to human agents based on available skill sets. We extend this basic concept to the routing of service requests from semi-autonomous vehicles to a centralized group of human operators based on their specific skills and aptitudes for servicing semi-autonomous vehicle intervention requests.